电商数仓项目----笔记七(数仓DIM层)
所谓的维度层其实就是分析数据的角度，维度层保存的表其实是分析数据的角度，比如：
--性别，年龄，品牌，品类
这层的表主要用于统计分析，因此DIM层的数据存储格式为orc列式存储+snappy压缩（时间短）
orc列式存储的好处：
- 查询的时候不需要扫描全部的数据，而只需要读取每次查询涉及的列，这样可以将I/O消耗降低N倍，另外可以保存每一列的统计信息(min、max、sum等)，实现部分的谓词下推。
- 由于每一列的成员都是同构的，可以针对不同的数据类型使用更高效的数据压缩算法，进一步减小I/O。
- 由于每一列的成员的同构性，可以使用更加适合CPU pipeline的编码方式，减小CPU的缓存失效。
维度表的设计
一个维度就是一张表，从实践的角度来讲，不同的维度就是这张表的字段，可以达到解耦的目的。如果维度特别简单，可以不用创建表，可以在事实表直接使用。
字段：只要能用来分析的维度，都是字段；
数据（字段）来源：参考业务数据的表字段：
-- 主维表：业务数据库主要用于分析维度字段的表；
-- 相关维表：业务数据库相关用于分析维度字段的表；
维度字段的确定：
尽可能生成丰富的维度属性：字段越多越好；
编码和文字共存（0男/1女）；
计算通用的维度属性；
下面举几个例子：
优惠券维度表
从主维表和相关维表分析：
主维表：coupon_info，相关维表：coupon_range，coupon_use，但是coupon_use算是一种行为概念，并不属于状态，状态才是用来做分析的。但是在coupon_info里面也有range相关字段，因此发生了冗余，只需关注coupon_info即可。
coupon_info长这样：
我们这样设计：
DROP TABLE IF EXISTS dim_coupon_full;
CREATE EXTERNAL TABLE dim_coupon_full
(
`id` STRING COMMENT '购物券编号',
`coupon_name` STRING COMMENT '购物券名称',
`coupon_type_code` STRING COMMENT '购物券类型编码',
`coupon_type_name` STRING COMMENT '购物券类型名称',
`condition_amount` DECIMAL(16, 2) COMMENT '满额数',
`condition_num` BIGINT COMMENT '满件数',
`activity_id` STRING COMMENT '活动编号',
`benefit_amount` DECIMAL(16, 2) COMMENT '减金额',
`benefit_discount` DECIMAL(16, 2) COMMENT '折扣',
`benefit_rule` STRING COMMENT '优惠规则:满元*减*元，满*件打*折',
`create_time` STRING COMMENT '创建时间',
`range_type_code` STRING COMMENT '优惠范围类型编码',
`range_type_name` STRING COMMENT '优惠范围类型名称',
`limit_num` BIGINT COMMENT '最多领取次数',
`taken_count` BIGINT COMMENT '已领取次数',
`start_time` STRING COMMENT '可以领取的开始日期',
`end_time` STRING COMMENT '可以领取的结束日期',
`operate_time` STRING COMMENT '修改时间',
`expire_time` STRING COMMENT '过期时间'
) COMMENT '优惠券维度表'
PARTITIONED BY (`dt` STRING)
STORED AS ORC
LOCATION '/warehouse/gmall/dim/dim_coupon_full/'
TBLPROPERTIES ('orc.compress' = 'snappy');其中不一样的地方有我们将ODS层原表的coupon_type分解为了coupon_type_code和coupon_type_name。将range_type分解为了range_type_code，range_type_name，并且增加了benefit_rule字段（优惠规则）。这样做符合我们上面说的编码和文字共存规则。
数据装载
我们的表主要从coupon_info和base_dic(字典表)中取得：
记住这里的主维表是coupon_info，因此我们先select coupon_info这张表，select里面的字段依照我们建表语句里面的字段先写好，当然其中肯定会有几个字段会报红，没关系我们后面还要join 操作，其中coupon_type_code，coupon_type_name，range_type_code，range_type_name字段是找不到的，因此需要join操作。我们join base_dic字典表：
join base_dic两次分别得到coupon_type_code，coupon_type_name字段和range_type_code，range_type_name字段；
接下来是benefit_rule字段，这里需要我们自行拼接。拼接逻辑如下：
case coupon_type
when '3201' then concat('满',condition_amount,'元减',benefit_amount,'元')
when '3202' then concat('满',condition_num,'件打',10*(1-benefit_discount),'折')
when '3203' then concat('减',benefit_amount,'元')
end benefit_rule,完整是这样：
select
id,
coupon_name,
coupon_type,
coupon_dic.dic_name,
condition_amount,
condition_num,
activity_id,
benefit_amount,
benefit_discount,
case coupon_type
when '3201' then concat('满',condition_amount,'元减',benefit_amount,'元')
when '3202' then concat('满',condition_num,'件打',10*(1-benefit_discount),'折')
when '3203' then concat('减',benefit_amount,'元')
end benefit_rule,
create_time,
range_type,
range_dic.dic_name,
limit_num,
taken_count,
start_time,
end_time,
operate_time,
expire_time
from
(
select
id,
coupon_name,
coupon_type,
condition_amount,
condition_num,
activity_id,
benefit_amount,
benefit_discount,
create_time,
range_type,
limit_num,
taken_count,
start_time,
end_time,
operate_time,
expire_time
from ods_coupon_info_full
where dt='2020-06-14'
)ci
left join
(
select
dic_code,
dic_name
from ods_base_dic_full
where dt='2020-06-14'
and parent_code='32'
)coupon_dic
on ci.coupon_type=coupon_dic.dic_code
left join
(
select
dic_code,
dic_name
from ods_base_dic_full
where dt='2020-06-14'
and parent_code='33'
)range_dic
on ci.range_type=range_dic.dic_code;数据装载我们只需要前面加上下面这一句即可：
insert overwrite table dim_coupon_full partition(dt='2020-06-14')我们的Dim层优惠券维度表就设计完啦。
活动维度表
同样的，找到主维表和相关维表。
activity_info ，activity_rule，activity_sku：我们分析的更多的是活动规则，而不是活动本身，所以主维表是activity_rule，相关维表是activity_info。
我们这样设计：
DROP TABLE IF EXISTS dim_activity_full;
CREATE EXTERNAL TABLE dim_activity_full
(
`activity_rule_id` STRING COMMENT '活动规则ID',
`activity_id` STRING COMMENT '活动ID',
`activity_name` STRING COMMENT '活动名称',
`activity_type_code` STRING COMMENT '活动类型编码',
`activity_type_name` STRING COMMENT '活动类型名称',
`activity_desc` STRING COMMENT '活动描述',
`start_time` STRING COMMENT '开始时间',
`end_time` STRING COMMENT '结束时间',
`create_time` STRING COMMENT '创建时间',
`condition_amount` DECIMAL(16, 2) COMMENT '满减金额',
`condition_num` BIGINT COMMENT '满减件数',
`benefit_amount` DECIMAL(16, 2) COMMENT '优惠金额',
`benefit_discount` DECIMAL(16, 2) COMMENT '优惠折扣',
`benefit_rule` STRING COMMENT '优惠规则',
`benefit_level` STRING COMMENT '优惠级别'
) COMMENT '活动信息表'
PARTITIONED BY (`dt` STRING)
STORED AS ORC
LOCATION '/warehouse/gmall/dim/dim_activity_full/'
TBLPROPERTIES ('orc.compress' = 'snappy');数据装载：
insert overwrite table dim_activity_full partition(dt='2020-06-14')
select
`activity_rule_id` ,--STRING COMMENT '活动规则ID',
`activity_id` ,--STRING COMMENT '活动ID',
`activity_name` ,--STRING COMMENT '活动名称',
`activity_type_code` ,--STRING COMMENT '活动类型编码',
`activity_type_name` ,--STRING COMMENT '活动类型名称',
`activity_desc` ,--STRING COMMENT '活动描述',
`start_time` ,--STRING COMMENT '开始时间',
`end_time` ,--STRING COMMENT '结束时间',
`create_time` ,--STRING COMMENT '创建时间',
`condition_amount` ,--DECIMAL(16, 2) COMMENT '满减金额',
`condition_num` ,--BIGINT COMMENT '满减件数',
`benefit_amount` ,--DECIMAL(16, 2) COMMENT '优惠金额',
`benefit_discount` ,--DECIMAL(16, 2) COMMENT '优惠折扣',
`benefit_rule` ,--STRING COMMENT '优惠规则',
`benefit_level` --STRING COMMENT '优惠级别'
from
(select
id `activity_rule_id` ,--STRING COMMENT '活动规则ID',
`activity_id` ,--STRING COMMENT '活动ID',
--`activity_name` ,--STRING COMMENT '活动名称',
activity_type `activity_type_code` ,--STRING COMMENT '活动类型编码',
--`activity_type_name` ,--STRING COMMENT '活动类型名称',
--`activity_desc` ,--STRING COMMENT '活动描述',
--`start_time` ,--STRING COMMENT '开始时间',
--`end_time` ,--STRING COMMENT '结束时间',
dt create_time ,--STRING COMMENT '创建时间',
`condition_amount` ,--DECIMAL(16, 2) COMMENT '满减金额',
`condition_num` ,--BIGINT COMMENT '满减件数',
`benefit_amount` ,--DECIMAL(16, 2) COMMENT '优惠金额',
`benefit_discount` ,--DECIMAL(16, 2) COMMENT '优惠折扣',
case activity_type
when '3101' then concat('满',condition_amount,'元减',benefit_amount,'元')
when '3102' then concat('满',condition_num,'件打',benefit_discount,'折')
when '3103' then concat('打',benefit_discount,'折')
end `benefit_rule` ,--STRING COMMENT '优惠规则',
`benefit_level` --STRING COMMENT '优惠级别'
from ods_activity_rule_full
where dt='2020-06-14')rule
left join
(
select
id,
activity_name,
activity_desc,
start_time,
end_time
from ods_activity_info_full
where dt='2020-06-14') info
on rule.activity_id=info.id
left join (
select
dic_code,
dic_name activity_type_name
from ods_base_dic_full
where dt='2020-06-14' and parent_code='31'
)dic on rule.activity_type_code=dic.dic_code整体思路就是先将create表中的字段复制到select 主维表的语句中，爆红的字段我们一一给他们join出来，或在join的那张表中给他们查询出来，这里就不详细分析了。
日期维度表
建表语句
DROP TABLE IF EXISTS dim_date;
CREATE EXTERNAL TABLE dim_date
(
`date_id` STRING COMMENT '日期ID',
`week_id` STRING COMMENT '周ID,一年中的第几周',
`week_day` STRING COMMENT '周几',
`day` STRING COMMENT '每月的第几天',
`month` STRING COMMENT '一年中的第几月',
`quarter` STRING COMMENT '一年中的第几季度',
`year` STRING COMMENT '年份',
`is_workday` STRING COMMENT '是否是工作日',
`holiday_id` STRING COMMENT '节假日'
) COMMENT '时间维度表'
STORED AS ORC
LOCATION '/warehouse/gmall/dim/dim_date/'
TBLPROPERTIES ('orc.compress' = 'snappy');数据装载
通常情况下，时间维度表的数据并不是来自于业务系统，而是手动写入，并且由于时间维度表数据的可预见性，无须每日导入，一般可一次性导入一年的数据。
（1）创建临时表
DROP TABLE IF EXISTS tmp_dim_date_info;
CREATE EXTERNAL TABLE tmp_dim_date_info (
`date_id` STRING COMMENT '日',
`week_id` STRING COMMENT '周ID',
`week_day` STRING COMMENT '周几',
`day` STRING COMMENT '每月的第几天',
`month` STRING COMMENT '第几月',
`quarter` STRING COMMENT '第几季度',
`year` STRING COMMENT '年',
`is_workday` STRING COMMENT '是否是工作日',
`holiday_id` STRING COMMENT '节假日'
) COMMENT '时间维度表'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/tmp/tmp_dim_date_info/';将数据文件上传到HFDS上临时表路径/warehouse/gmall/tmp/tmp_dim_date_info
（3）执行以下语句将其导入时间维度表
insert overwrite table dim_date select * from tmp_dim_date_info;
用户维度表
用户维度表这里我们使用拉链表，来记录用户姓名的变更或者用户的增加减少。
（1）数据装载过程
（2）数据流向
首日装载
我们的用户数据在进行首日装载和后续的变更都是insert overwrite到9999-12-31的分区，首日装载如下：
insert overwrite table dim_user_zip partition (dt='9999-12-31')
select
data.id,
data.login_name,
data.nick_name,
md5(data.name),
md5(data.phone_num),
md5(data.email),
data.user_level,
data.birthday,
data.gender,
data.create_time,
data.operate_time,
'2020-06-14' start_date,
'9999-12-31' end_date
from ods_user_info_inc
where dt='2020-06-14'
and type='bootstrap-insert';每日装载
装载思路：
装载语句:
with
tmp as
(
select
old.id old_id,
old.login_name old_login_name,
old.nick_name old_nick_name,
old.name old_name,
old.phone_num old_phone_num,
old.email old_email,
old.user_level old_user_level,
old.birthday old_birthday,
old.gender old_gender,
old.create_time old_create_time,
old.operate_time old_operate_time,
old.start_date old_start_date,
old.end_date old_end_date,
new.id new_id,
new.login_name new_login_name,
new.nick_name new_nick_name,
new.name new_name,
new.phone_num new_phone_num,
new.email new_email,
new.user_level new_user_level,
new.birthday new_birthday,
new.gender new_gender,
new.create_time new_create_time,
new.operate_time new_operate_time,
new.start_date new_start_date,
new.end_date new_end_date
from
(
select
id,
login_name,
nick_name,
name,
phone_num,
email,
user_level,
birthday,
gender,
create_time,
operate_time,
start_date,
end_date
from dim_user_zip
where dt='9999-12-31'
)old
full outer join
(
select
id,
login_name,
nick_name,
md5(name) name,
md5(phone_num) phone_num,
md5(email) email,
user_level,
birthday,
gender,
create_time,
operate_time,
'2020-06-15' start_date,
'9999-12-31' end_date
from
(
select
data.id,
data.login_name,
data.nick_name,
data.name,
data.phone_num,
data.email,
data.user_level,
data.birthday,
data.gender,
data.create_time,
data.operate_time,
row_number() over (partition by data.id order by ts desc) rn
from ods_user_info_inc
where dt='2020-06-15'
)t1
where rn=1
)new
on old.id=new.id
)
insert overwrite table dim_user_zip partition(dt)
select
if(new_id is not null,new_id,old_id),
if(new_id is not null,new_login_name,old_login_name),
if(new_id is not null,new_nick_name,old_nick_name),
if(new_id is not null,new_name,old_name),
if(new_id is not null,new_phone_num,old_phone_num),
if(new_id is not null,new_email,old_email),
if(new_id is not null,new_user_level,old_user_level),
if(new_id is not null,new_birthday,old_birthday),
if(new_id is not null,new_gender,old_gender),
if(new_id is not null,new_create_time,old_create_time),
if(new_id is not null,new_operate_time,old_operate_time),
if(new_id is not null,new_start_date,old_start_date),
if(new_id is not null,new_end_date,old_end_date),
if(new_id is not null,new_end_date,old_end_date) dt
from tmp
union all
select
old_id,
old_login_name,
old_nick_name,
old_name,
old_phone_num,
old_email,
old_user_level,
old_birthday,
old_gender,
old_create_time,
old_operate_time,
old_start_date,
cast(date_add('2020-06-15',-1) as string) old_end_date,
cast(date_add('2020-06-15',-1) as string) dt
from tmp
where old_id is not null
and new_id is not null;
数据装载脚本
首日装载脚本
#!/bin/bash
APP=gmall
if [ -n "$2" ] ;then
do_date=$2
else
echo "请传入日期参数"
exit
fi
dim_user_zip="
insert overwrite table ${APP}.dim_user_zip partition (dt='9999-12-31')
select
data.id,
data.login_name,
data.nick_name,
md5(data.name),
md5(data.phone_num),
md5(data.email),
data.user_level,
data.birthday,
data.gender,
data.create_time,
data.operate_time,
'$do_date' start_date,
'9999-12-31' end_date
from ${APP}.ods_user_info_inc
where dt='$do_date'
and type='bootstrap-insert';
"
dim_sku_full="
with
sku as
(
select
id,
price,
sku_name,
sku_desc,
weight,
is_sale,
spu_id,
category3_id,
tm_id,
create_time
from ${APP}.ods_sku_info_full
where dt='$do_date'
),
spu as
(
select
id,
spu_name
from ${APP}.ods_spu_info_full
where dt='$do_date'
),
c3 as
(
select
id,
name,
category2_id
from ${APP}.ods_base_category3_full
where dt='$do_date'
),
c2 as
(
select
id,
name,
category1_id
from ${APP}.ods_base_category2_full
where dt='$do_date'
),
c1 as
(
select
id,
name
from ${APP}.ods_base_category1_full
where dt='$do_date'
),
tm as
(
select
id,
tm_name
from ${APP}.ods_base_trademark_full
where dt='$do_date'
),
attr as
(
select
sku_id,
collect_set(named_struct('attr_id',attr_id,'value_id',value_id,'attr_name',attr_name,'value_name',value_name)) attrs
from ${APP}.ods_sku_attr_value_full
where dt='$do_date'
group by sku_id
),
sale_attr as
(
select
sku_id,
collect_set(named_struct('sale_attr_id',sale_attr_id,'sale_attr_value_id',sale_attr_value_id,'sale_attr_name',sale_attr_name,'sale_attr_value_name',sale_attr_value_name)) sale_attrs
from ${APP}.ods_sku_sale_attr_value_full
where dt='$do_date'
group by sku_id
)
insert overwrite table ${APP}.dim_sku_full partition(dt='$do_date')
select
sku.id,
sku.price,
sku.sku_name,
sku.sku_desc,
sku.weight,
sku.is_sale,
sku.spu_id,
spu.spu_name,
sku.category3_id,
c3.name,
c3.category2_id,
c2.name,
c2.category1_id,
c1.name,
sku.tm_id,
tm.tm_name,
attr.attrs,
sale_attr.sale_attrs,
sku.create_time
from sku
left join spu on sku.spu_id=spu.id
left join c3 on sku.category3_id=c3.id
left join c2 on c3.category2_id=c2.id
left join c1 on c2.category1_id=c1.id
left join tm on sku.tm_id=tm.id
left join attr on sku.id=attr.sku_id
left join sale_attr on sku.id=sale_attr.sku_id;
"
dim_province_full="
insert overwrite table ${APP}.dim_province_full partition(dt='$do_date')
select
province.id,
province.name,
province.area_code,
province.iso_code,
province.iso_3166_2,
region_id,
region_name
from
(
select
id,
name,
region_id,
area_code,
iso_code,
iso_3166_2
from ${APP}.ods_base_province_full
where dt='$do_date'
)province
left join
(
select
id,
region_name
from ${APP}.ods_base_region_full
where dt='$do_date'
)region
on province.region_id=region.id;
"
dim_coupon_full="
insert overwrite table ${APP}.dim_coupon_full partition(dt='$do_date')
select
id,
coupon_name,
coupon_type,
coupon_dic.dic_name,
condition_amount,
condition_num,
activity_id,
benefit_amount,
benefit_discount,
case coupon_type
when '3201' then concat('满',condition_amount,'元减',benefit_amount,'元')
when '3202' then concat('满',condition_num,'件打',10*(1-benefit_discount),'折')
when '3203' then concat('减',benefit_amount,'元')
end benefit_rule,
create_time,
range_type,
range_dic.dic_name,
limit_num,
taken_count,
start_time,
end_time,
operate_time,
expire_time
from
(
select
id,
coupon_name,
coupon_type,
condition_amount,
condition_num,
activity_id,
benefit_amount,
benefit_discount,
create_time,
range_type,
limit_num,
taken_count,
start_time,
end_time,
operate_time,
expire_time
from ${APP}.ods_coupon_info_full
where dt='$do_date'
)ci
left join
(
select
dic_code,
dic_name
from ${APP}.ods_base_dic_full
where dt='$do_date'
and parent_code='32'
)coupon_dic
on ci.coupon_type=coupon_dic.dic_code
left join
(
select
dic_code,
dic_name
from ${APP}.ods_base_dic_full
where dt='$do_date'
and parent_code='33'
)range_dic
on ci.range_type=range_dic.dic_code;
"
dim_activity_full="
insert overwrite table ${APP}.dim_activity_full partition(dt='$do_date')
select
rule.id,
info.id,
activity_name,
rule.activity_type,
dic.dic_name,
activity_desc,
start_time,
end_time,
create_time,
condition_amount,
condition_num,
benefit_amount,
benefit_discount,
case rule.activity_type
when '3101' then concat('满',condition_amount,'元减',benefit_amount,'元')
when '3102' then concat('满',condition_num,'件打',10*(1-benefit_discount),'折')
when '3103' then concat('打',10*(1-benefit_discount),'折')
end benefit_rule,
benefit_level
from
(
select
id,
activity_id,
activity_type,
condition_amount,
condition_num,
benefit_amount,
benefit_discount,
benefit_level
from ${APP}.ods_activity_rule_full
where dt='$do_date'
)rule
left join
(
select
id,
activity_name,
activity_type,
activity_desc,
start_time,
end_time,
create_time
from ${APP}.ods_activity_info_full
where dt='$do_date'
)info
on rule.activity_id=info.id
left join
(
select
dic_code,
dic_name
from ${APP}.ods_base_dic_full
where dt='$do_date'
and parent_code='31'
)dic
on rule.activity_type=dic.dic_code;
"
case $1 in
"dim_user_zip")
hive -e "$dim_user_zip"
;;
"dim_sku_full")
hive -e "$dim_sku_full"
;;
"dim_province_full")
hive -e "$dim_province_full"
;;
"dim_coupon_full")
hive -e "$dim_coupon_full"
;;
"dim_activity_full")
hive -e "$dim_activity_full"
;;
"all")
hive -e "$dim_user_zip$dim_sku_full$dim_province_full$dim_coupon_full$dim_activity_full"
;;
esac每日装载脚本
#!/bin/bash
APP=gmall
# 如果是输入的日期按照取输入日期；如果没输入日期取当前时间的前一天
if [ -n "$2" ] ;then
do_date=$2
else
do_date=`date -d "-1 day" +%F`
fi
dim_user_zip="
set hive.exec.dynamic.partition.mode=nonstrict;
with
tmp as
(
select
old.id old_id,
old.login_name old_login_name,
old.nick_name old_nick_name,
old.name old_name,
old.phone_num old_phone_num,
old.email old_email,
old.user_level old_user_level,
old.birthday old_birthday,
old.gender old_gender,
old.create_time old_create_time,
old.operate_time old_operate_time,
old.start_date old_start_date,
old.end_date old_end_date,
new.id new_id,
new.login_name new_login_name,
new.nick_name new_nick_name,
new.name new_name,
new.phone_num new_phone_num,
new.email new_email,
new.user_level new_user_level,
new.birthday new_birthday,
new.gender new_gender,
new.create_time new_create_time,
new.operate_time new_operate_time,
new.start_date new_start_date,
new.end_date new_end_date
from
(
select
id,
login_name,
nick_name,
name,
phone_num,
email,
user_level,
birthday,
gender,
create_time,
operate_time,
start_date,
end_date
from ${APP}.dim_user_zip
where dt='9999-12-31'
)old
full outer join
(
select
id,
login_name,
nick_name,
md5(name) name,
md5(phone_num) phone_num,
md5(email) email,
user_level,
birthday,
gender,
create_time,
operate_time,
'$do_date' start_date,
'9999-12-31' end_date
from
(
select
data.id,
data.login_name,
data.nick_name,
data.name,
data.phone_num,
data.email,
data.user_level,
data.birthday,
data.gender,
data.create_time,
data.operate_time,
row_number() over (partition by data.id order by ts desc) rn
from ${APP}.ods_user_info_inc
where dt='$do_date'
)t1
where rn=1
)new
on old.id=new.id
)
insert overwrite table ${APP}.dim_user_zip partition(dt)
select
if(new_id is not null,new_id,old_id),
if(new_id is not null,new_login_name,old_login_name),
if(new_id is not null,new_nick_name,old_nick_name),
if(new_id is not null,new_name,old_name),
if(new_id is not null,new_phone_num,old_phone_num),
if(new_id is not null,new_email,old_email),
if(new_id is not null,new_user_level,old_user_level),
if(new_id is not null,new_birthday,old_birthday),
if(new_id is not null,new_gender,old_gender),
if(new_id is not null,new_create_time,old_create_time),
if(new_id is not null,new_operate_time,old_operate_time),
if(new_id is not null,new_start_date,old_start_date),
if(new_id is not null,new_end_date,old_end_date),
if(new_id is not null,new_end_date,old_end_date) dt
from tmp
union all
select
old_id,
old_login_name,
old_nick_name,
old_name,
old_phone_num,
old_email,
old_user_level,
old_birthday,
old_gender,
old_create_time,
old_operate_time,
old_start_date,
cast(date_add('$do_date',-1) as string) old_end_date,
cast(date_add('$do_date',-1) as string) dt
from tmp
where old_id is not null
and new_id is not null;
"
dim_sku_full="
with
sku as
(
select
id,
price,
sku_name,
sku_desc,
weight,
is_sale,
spu_id,
category3_id,
tm_id,
create_time
from ${APP}.ods_sku_info_full
where dt='$do_date'
),
spu as
(
select
id,
spu_name
from ${APP}.ods_spu_info_full
where dt='$do_date'
),
c3 as
(
select
id,
name,
category2_id
from ${APP}.ods_base_category3_full
where dt='$do_date'
),
c2 as
(
select
id,
name,
category1_id
from ${APP}.ods_base_category2_full
where dt='$do_date'
),
c1 as
(
select
id,
name
from ${APP}.ods_base_category1_full
where dt='$do_date'
),
tm as
(
select
id,
tm_name
from ${APP}.ods_base_trademark_full
where dt='$do_date'
),
attr as
(
select
sku_id,
collect_set(named_struct('attr_id',attr_id,'value_id',value_id,'attr_name',attr_name,'value_name',value_name)) attrs
from ${APP}.ods_sku_attr_value_full
where dt='$do_date'
group by sku_id
),
sale_attr as
(
select
sku_id,
collect_set(named_struct('sale_attr_id',sale_attr_id,'sale_attr_value_id',sale_attr_value_id,'sale_attr_name',sale_attr_name,'sale_attr_value_name',sale_attr_value_name)) sale_attrs
from ${APP}.ods_sku_sale_attr_value_full
where dt='$do_date'
group by sku_id
)
insert overwrite table ${APP}.dim_sku_full partition(dt='$do_date')
select
sku.id,
sku.price,
sku.sku_name,
sku.sku_desc,
sku.weight,
sku.is_sale,
sku.spu_id,
spu.spu_name,
sku.category3_id,
c3.name,
c3.category2_id,
c2.name,
c2.category1_id,
c1.name,
sku.tm_id,
tm.tm_name,
attr.attrs,
sale_attr.sale_attrs,
sku.create_time
from sku
left join spu on sku.spu_id=spu.id
left join c3 on sku.category3_id=c3.id
left join c2 on c3.category2_id=c2.id
left join c1 on c2.category1_id=c1.id
left join tm on sku.tm_id=tm.id
left join attr on sku.id=attr.sku_id
left join sale_attr on sku.id=sale_attr.sku_id;
"
dim_province_full="
insert overwrite table ${APP}.dim_province_full partition(dt='$do_date')
select
province.id,
province.name,
province.area_code,
province.iso_code,
province.iso_3166_2,
region_id,
region_name
from
(
select
id,
name,
region_id,
area_code,
iso_code,
iso_3166_2
from ${APP}.ods_base_province_full
where dt='$do_date'
)province
left join
(
select
id,
region_name
from ${APP}.ods_base_region_full
where dt='$do_date'
)region
on province.region_id=region.id;
"
dim_coupon_full="
insert overwrite table ${APP}.dim_coupon_full partition(dt='$do_date')
select
id,
coupon_name,
coupon_type,
coupon_dic.dic_name,
condition_amount,
condition_num,
activity_id,
benefit_amount,
benefit_discount,
case coupon_type
when '3201' then concat('满',condition_amount,'元减',benefit_amount,'元')
when '3202' then concat('满',condition_num,'件打',10*(1-benefit_discount),'折')
when '3203' then concat('减',benefit_amount,'元')
end benefit_rule,
create_time,
range_type,
range_dic.dic_name,
limit_num,
taken_count,
start_time,
end_time,
operate_time,
expire_time
from
(
select
id,
coupon_name,
coupon_type,
condition_amount,
condition_num,
activity_id,
benefit_amount,
benefit_discount,
create_time,
range_type,
limit_num,
taken_count,
start_time,
end_time,
operate_time,
expire_time
from ${APP}.ods_coupon_info_full
where dt='$do_date'
)ci
left join
(
select
dic_code,
dic_name
from ${APP}.ods_base_dic_full
where dt='$do_date'
and parent_code='32'
)coupon_dic
on ci.coupon_type=coupon_dic.dic_code
left join
(
select
dic_code,
dic_name
from ${APP}.ods_base_dic_full
where dt='$do_date'
and parent_code='33'
)range_dic
on ci.range_type=range_dic.dic_code;
"
dim_activity_full="
insert overwrite table ${APP}.dim_activity_full partition(dt='$do_date')
select
rule.id,
info.id,
activity_name,
rule.activity_type,
dic.dic_name,
activity_desc,
start_time,
end_time,
create_time,
condition_amount,
condition_num,
benefit_amount,
benefit_discount,
case rule.activity_type
when '3101' then concat('满',condition_amount,'元减',benefit_amount,'元')
when '3102' then concat('满',condition_num,'件打',10*(1-benefit_discount),'折')
when '3103' then concat('打',10*(1-benefit_discount),'折')
end benefit_rule,
benefit_level
from
(
select
id,
activity_id,
activity_type,
condition_amount,
condition_num,
benefit_amount,
benefit_discount,
benefit_level
from ${APP}.ods_activity_rule_full
where dt='$do_date'
)rule
left join
(
select
id,
activity_name,
activity_type,
activity_desc,
start_time,
end_time,
create_time
from ${APP}.ods_activity_info_full
where dt='$do_date'
)info
on rule.activity_id=info.id
left join
(
select
dic_code,
dic_name
from ${APP}.ods_base_dic_full
where dt='$do_date'
and parent_code='31'
)dic
on rule.activity_type=dic.dic_code;
"
case $1 in
"dim_user_zip")
hive -e "$dim_user_zip"
;;
"dim_sku_full")
hive -e "$dim_sku_full"
;;
"dim_province_full")
hive -e "$dim_province_full"
;;
"dim_coupon_full")
hive -e "$dim_coupon_full"
;;
"dim_activity_full")
hive -e "$dim_activity_full"
;;
"all")
hive -e "$dim_user_zip$dim_sku_full$dim_province_full$dim_coupon_full$dim_activity_full"
;;
esac